Multi-Objective Topological Optimization of Support Frame for High-Speed and Heavy-Load Triangle Track Wheel Based on Analytic Hierarchy Process
Bibliographic record
Abstract
The high-speed, heavy-load and changeable triangle track wheel is a motion device that can carry out interchange between the track wheel and tire in an ordinary vehicle. The topology optimization for the support frame can reduce weight and improve the maneuverability of the vehicle. However, it is difficult to consider simultaneously its weight, stiffness and modal in the process of the structure optimization. Thus, a topology optimization method for multi-objective and multi-working-condition is proposed based on the AHP (analytic hierarchy process) and average frequency method. Firstly, considering the static multi-stiffness target and dynamic vibration frequency target, using the compromise programming method and average frequency method, the objective function of the multi-objective and multi-working-condition topology optimization is established. Then, based on the optimization target, design criteria and indexes, the lightweight hierarchical structure model of the support frame consisting of three levels and eight weight factors is established. Values of 8 weight coefficients of the multi-objective topology optimization are determined through solving the weight factor judgment matrix. Finally, considering the multi-working-condition, taking the minimum objective function of the static and dynamic characteristics as target, and the volume ratio is 50% as boundary, the mathematical model of the topology optimization is established. Simulation results show that the stiffness and strength of the support frame are improved respectively by 74.3% and 1.3% while its weight is reduced by 16.3%. This method also provides a new way to the lightweight design for other large, heavy and multi-condition equipment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".